Hands-On Meta Learning with Python

Some of the theory is fairly good and clear for this book, but the code is an awful mess. There are not clear reasons why the author selected examples for certain examples. The code is also a mix of Python 2 and Python 3, so you need to have both installed. You could convert the code to Python 3, but that could be a lot of work and error prone. One problem with the text of the book is that the author used images for some of the symbols, and they don't render well for the kindle version. It is really hard to read them. I have tried reading on pc and on my phone, but some of the symbols are just impossible to read. There are some small problems with writing style, but those can be overlooked.

I do not recommend this book for serious readers. Most of his code comes from other's repositories on GitHub.

The book is very nice and the explanation is pretty clear and neat. I loved the model agnostic meta learning chapter as the author has broken down such complex topic into very simple explanation.One of the advantages of this book is it covers from the few short learning algorithms to the state of the art MAML algorithms.

I stumbled upon this book's GitHub repo and bought this book. It's very well written and the algorithms are explained in the simplest way I could ever think of.Must Read for anyone willing to learn Meta-Learning!!!.

The code base does not work when you run it on your own machine, azure notebooks, or on google colab. When you look up errors you see that the writer copied and pasted code from the internet. This book is a ripoff. The writer has no clue what he is doing.

It's pretty clear that the author does not know much (if anything) about the subject. The book consists of bad, copy-pasted blog posts "stapled together". The math is very badly formatted, and the copy-pasted code is broken.